
# Import packages

import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt


# Load dataset

df = pd.read_csv("~/Desktop/AirBnbNYC2019.csv") 
df.head()


# Identify variable types

def describe_data(df):
    print("Data Types:")
    print(df.dtypes)
    print("Rows and Columns:")
    print(df.shape)
    
describe_data(df)

Data Types:
id                                  int64
name                               object
host_id                             int64
host_name                          object
neighbourhood_group                object
neighbourhood                      object
latitude                          float64
longitude                         float64
room_type                          object
price                               int64
minimum_nights                      int64
number_of_reviews                   int64
last_review                        object
reviews_per_month                 float64
calculated_host_listings_count      int64
availability_365                    int64
dtype: object
Rows and Columns:
(48895, 16)


# Identify column names for further analysis
 
list(df.columns)

['id',
 'name',
 'host_id',
 'host_name',
 'neighbourhood_group',
 'neighbourhood',
 'latitude',
 'longitude',
 'room_type',
 'price',
 'minimum_nights',
 'number_of_reviews',
 'last_review',
 'reviews_per_month',
 'calculated_host_listings_count',
 'availability_365']


# Identify unique borough names

df['neighbourhood_group'].unique()

array(['Brooklyn', 'Manhattan', 'Queens', 'Staten Island', 'Bronx'],
      dtype=object)


# Create an interactive distribution map for AirBNB locations in NYC.

import pandas as pd
import folium

m1 = folium.Map(location=[40.693943, -73.985880], default_zoom_start=10, width=640, height=480)

for lat, lon, label in zip(df.latitude, df.longitude, df.neighbourhood.astype(str)):
    if label!='0':
        folium.features.CircleMarker(
            [lat, lon],
            radius=0.1,
            color='#0f4fff',
            color_opacity=0.6
        ).add_to(m1)

m1


# Check location frequencies for each boroughs

BoroCounts = df.neighbourhood_group.value_counts()
print("-------Frequency counts for BORO column-------\n",BoroCounts)
print(type(BoroCounts))

-------Frequency counts for BORO column-------
 Manhattan        21661
Brooklyn         20104
Queens            5666
Bronx             1091
Staten Island      373
Name: neighbourhood_group, dtype: int64
<class 'pandas.core.series.Series'>


# Connstruct a pie chart for above summary

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl

counts = pd.Series([44.3, 41.1, 11.6, 2.2, 0.8], 
                   index=['Manhattan (44.3%)', 'Brooklyn (41.1%)', 'Queens (11.6%)', 
                          'Bronx (2.2%)', 'Staten Island (0.8%)'])

explode = (0,0,0.1,0.2,0.3)
colors = ['#666666', '#911818', '#f4db1a', '#23079c', '#347a06']

counts.plot(kind='pie', fontsize=12, colors=colors, explode=explode)
plt.axis('equal')
plt.ylabel('')
plt.show()

	id	name	host_id	host_name	neighbourhood_group	neighbourhood	latitude	longitude	room_type	price	minimum_nights	number_of_reviews	last_review	reviews_per_month	calculated_host_listings_count	availability_365
0	2539	Clean & quiet apt home by the park	2787	John	Brooklyn	Kensington	40.64749	-73.97237	Private room	149	1	9	2018-10-19	0.21	6	365
1	2595	Skylit Midtown Castle	2845	Jennifer	Manhattan	Midtown	40.75362	-73.98377	Entire home/apt	225	1	45	2019-05-21	0.38	2	355
2	3647	THE VILLAGE OF HARLEM....NEW YORK !	4632	Elisabeth	Manhattan	Harlem	40.80902	-73.94190	Private room	150	3	0	NaN	NaN	1	365
3	3831	Cozy Entire Floor of Brownstone	4869	LisaRoxanne	Brooklyn	Clinton Hill	40.68514	-73.95976	Entire home/apt	89	1	270	2019-07-05	4.64	1	194
4	5022	Entire Apt: Spacious Studio/Loft by central park	7192	Laura	Manhattan	East Harlem	40.79851	-73.94399	Entire home/apt	80	10	9	2018-11-19	0.10	1	0

< EDA : Location Distribution >¶